1. Technical Field
The present invention relates generally to scalable video coders, and more specifically to a system and method that incorporates an optimal design for an entropy constrained scalar quantizer (ECSQ) for a Laplace-Markov source model.
2. Related Art
As the popularity of systems utilizing compressed video and data signals continues to grow, improving compression performance remains an ongoing challenge. Various compression standards, including MPEG-4 (Moving Picture Expert Group) and H.263 video compression, provide for various mechanisms, including base layer and enhancement layer coding, to improve compression efficiency.
One particular challenge involves applications requiring a scalable bitstream, which have become a common requirement for many coding and transmission systems. Many applications, including multiparty video conferencing and multicast over the Internet, require the compressed information to be simultaneously transmitted to multiple receivers over different communication links. A scalable bitstream is one that allows decoding at a variety of bit rates (and corresponding levels of quality), where the lower rate information streams are embedded within the higher rate bitstreams in a manner that minimizes redundancy. Unfortunately, the scalability necessary to implement such a system usually has a negative impact on the overall compression efficiency.
Recently, a new efficient scalable video coding algorithm has been proposed in which the coding efficiency can be increased using an estimation theoretic approach. This approach consistently outperforms the existing signal-to-noise ratio (SNR) scalable video coders for all rates. In this algorithm, the evolution of the discrete coefficient transform (DCT) coefficients is modeled as a Laplace-Markov process, and can be implemented in a state-of-the-art encoder that optimally combines the quantizer information of both the base layer and the enhancement layer. Unlike a conventional scalable video coder, the state-of-the-art coder uses information from the base layer and enhancement layer DCT coefficients to optimally predict and reconstruct in an estimation theoretic (ET) framework. Greater control over the quantizer information results in potentially better performance since the quantizer information affects both the distortion and the bitrate of the system. Such a methodology is described in “Toward Optimality in Scalable Predictive Coding,” by Kenneth Rose and Shankar L. Regunathan, IEEE Trans. on Image Processing, vol. 10, no. 7, 965, July 2001, which is hereby incorporated by reference.
While such recent state-of-the art coders using the above-mentioned algorithms have clearly demonstrated advantages over prior art scalable video coders, they have failed to address the quantizer design. Rather such systems presently utilize a conventional uniform threshold quantizer (UTQ) with a “deadzone.” (See, e.g., G. Sullivan, “Efficient scalar quantization of exponential and Laplacian random variables,” IEEE Trans.on Information Theory, vol. 42, no. 5, pp. 1365–1374, September 1996, which is hereby incorporated by reference.)
Since this type of quantizer has not been designed for a Laplace-Markov source model, a loss of coding efficiency may result. Accordingly, without an optimal quantizer design, coders employing a Laplace-Markov source model will fail to provide the best possible performance.